How to get good results with GAN and some thousands of images?
With a Google Cloud V100 GPU the GAN would run a week to two with
  default parameters. Does this sound realistic time for this kind of
  dataset? It's definitely not feasible for me.


Yes, V100s are quite beefy. You shouldn't even need a week. Obviously this is based on my experience with various problems, rather than a concrete calculation.


  Is 4000 images enough to train a GAN from scratch?


For the size you want to generate, it is still on the edge of what would constitute a decent training set. You will get some results(depending on architecture) but will probably want to grab more data if at all possible.


  Is there any implementation with pytorch/keras that would be good to
  get nice results with?


I would check out this link: https://github.com/eriklindernoren/Keras-GAN. It has some nice implementations in Keras. As far as the particular GAN to use, I would start out with a vanilla GAN that fits your purposes and focus on toying with hyperparameters, and if that doesn't work, look into one of the other variations that correlate well with your problem.